AI in M&A is moving from experiment to standard practice: what that means for deal teams

AI in M&A: what the research says and what actually works in practice

The evidence is in. Teams using AI in M&A report an average cost reduction of roughly 20 percent, and 40 percent of respondents say AI enabled 30 to 50 percent faster deal cycles. That is no longer a pilot result. It reflects how a growing number of deal teams are working today.

But the same research points to something worth paying attention to: the large majority of practitioners currently rely on commercially available AI chatbots, not customised, proprietary tools. That gap between the tools most teams are using and the tools that actually deliver in a professional deal context is where a lot of the frustration lives.

Here is where AI creates genuine value in M&A, and what it takes to deliver that reliably.

Target identification: the biggest opportunity, if done right

There is a surfeit of potential companies to acquire, sell to, or partner with, and a huge amount of data about these companies is obtainable. The most successful M&A programs look beyond their core business into adjacencies and potential step-outs.

The problem with general-purpose AI tools for this work is structural. They pull from surface-level web content. They have no access to company registries, financial statements, or ownership data. They cannot apply hard filters by revenue, headcount, ownership type, or geography. And they miss the companies that matter most in the mid-market: niche players and private companies with little online presence but strong fundamentals.

StrategyBridgeAI's longlist functionality is built specifically for this. The platform searches across around 50 million private and public companies using a semantic chat interface, finding targets, buyers, and competitors based on business model logic, market position, size, ownership structure, and location. This sits alongside deterministic filter logic, so results are not prompt-approximations but clean, filtered datasets ready for downstream use.

The output covers financial KPIs, ownership structures, qualitative company data, transactions, and contact information, all exportable directly to Excel in the user's own format.

Outside-in business analysis: from first view to investment decision

Target assessment needs to encompass several dimensions to identify the highest-value potential targets with the right strategic and financial fit. That means going beyond a longlist: understanding where a company sits in its market, how it performs against peers, and whether the numbers hold up.

This is where most teams still lose significant time. Pulling together a credible outside-in analysis, peer benchmarking, and a valuation range manually takes days. StrategyBridgeAI compresses that into minutes.

The outside-in business analysis module delivers a full company assessment at the click of a button:

• Financials and KPIs pulled from verified sources and structured automatically
• Peer group benchmarking based on structural business model similarity, not industry codes
• Valuation calculated deterministically using established financial methodologies, then analytically interpreted with AI-generated insights
• Forecasting grounded in market data and peer trends
• Strengths, risks, and value drivers surfaced from the data, not generated from thin air

Every calculation is traceable. Every output is audit-grade. And the final report is delivered directly in the client's own design, ready for an investment committee, a pitch, or a credit decision.

As Tobias Nellinger of dhmp puts it:

"Analyses are now available faster and at the same time much more comprehensive than before."

Due diligence and beyond

Gen AI helps companies accelerate diligence and augment integration planning, with teams that engage early developing the necessary documentation, inputs, and systems they need as the next wave of innovation arrives.

For practitioners in audit, restructuring, and corporate finance, the same logic applies. Outside-in analysis supports red-flag due diligence. Benchmarking supports fairness opinions and valuation sign-off. Market intelligence supports strategic planning and pitch preparation. All of it from one platform, with consistent methodology and auditable outputs.

StrategyBridgeAI is listed by the Institut der Wirtschaftsprüfer (IDW), which reflects the quality standard the platform is built to.

The gap between potential and practice

Only 30 percent of M&A practitioners engage with AI at moderate to high levels, and a lack of expertise is identified as the main challenge to adoption. That is not surprising. Generic AI tools require significant prompt engineering, produce inconsistent results, and cannot be relied upon for professional-grade outputs.

The teams moving fastest are not the ones with the most AI expertise. They are the ones using tools built for their specific work, with verified data, transparent methodology, and outputs that hold up under scrutiny.

Hendrik Rathje of Möhrle Happ Luther puts it directly:

"Everyone wants to use AI, but if you try it with a do-it-yourself solution, the quality simply cannot match what a specific tool delivers. For that, our industry has StrategyBridgeAI. It will simply become the market standard."

Read the full client story

Book a demo to see the platform in practice. Or explore the full platform overview here.

McKinsey & Company: Gen AI in M&A: a new opportunity for growth, May 2024McKinsey & Company: Gen AI in M&A: from theory to practice to high performance, January 2026